The use of insensitive global measures, which do not adequately capture lung complexity and may be only minimally altered by significant local disease, not only foments an incomplete understanding of lung pathophysiology but also results in the need to study large numbers of subjects over long time periods in order to evaluate new treatments. Image-based measures, including evaluation of static and dynamic structure and function, are now recognized as very sensitive indicators of localized subclinical disease and appear to much better describe these complex lung processes. Small changes are easily detected and quantified, particularly using computer-aided analysis, resulting in a more rapid and more objective assessment of disease progression and therapeutic outcomes. As a group of interrelated investigators studying the heart and lungs through imaging, our image data sets along with the computational tasks and the visualization of the data have brought us to the need for resources well beyond conventional desk top computers or PC clusters. In this request for a shared instrumentation grant, we outline the need for a shared computation and visualization cluster system to simultaneously provide for supercomputer-level computational performance, the ability for a single process to link to large RAM, to interactively visualize large data sets, and to link both of these to closely housed large online data storage capacity. Our long-term objective is to advance pulmonary medicine through use of imaging-based, computer-aided approaches for quantitative evaluation of lung as well as heart structure and function. The specific aims are to: 1) overcome the computational bottlenecks imposed by our limited computing resources that hinder our progress on computational pulmonary fluid dynamics and lung tissue biomechanics, texture analysis, image matching and registration, and four-dimensional (4D) imaging and data analyses, and 2) share our experience and resources with a broader research community. The proposed cluster system will allow us to achieve the first aim by providing the computing power, display and analysis environment which is critical for our CT, MRI and Micro CT analyses, the computational fluid and structural mechanics studies, and 4D quantitative image assessment on a large population of healthy and diseased human subjects for study of lung-morphology-pathophysiology relationships. The second aim will be achieved by forming a broadly-based internal advisory committee to assisting with recruitment of new cluster users and collaborators from different disciplines in relation to biomedical engineering, health sciences and medicine. The proposed system will help us to achieve and exceed the research objectives proposed in our funded NIH projects. It can further lead to a better description and understanding of the human lung and heart and their response to disease, injury, and treatment- based not upon single global measures but upon quantifiable regional features, which is fundamentally important to the future of pulmonary medicine. [unreadable] [unreadable] [unreadable]